Kurtosis with Skew Crossover Focused Oscillator

Description:
This indicator highlights Skewness/Kurtosis crossovers for short-term trading:

Green upward arrows: Skew crosses above Kurtosis → potential long signal.

Red downward arrows: Skew crosses below Kurtosis → potential short signal.

Yellow upward arrows: Extreme negative skew (skew ≤ -1.7) → potential oversold/reversal opportunity.

Oscillator Pane:

Orange = Skewness (smoothed)

Blue = Kurtosis (adjusted, smoothed)

Zero line = visual reference

Usage:

Primarily for 2–5 minute charts, highlighting statistical anomalies and potential short-term reversals that can be used in conjunction with OBV and/or CVD

Arrows signal potential entries based on skew/kurt dynamics.




Potential ideas???????
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Add Supporting Market Context
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Currently, signals are purely based on skew/kurt crossovers. Adding supporting indicators could improve reliability:

Volume / CVD: Identify when crossovers occur with real buying/selling pressure.

Wick Imbalance: Detect forced moves in price structure.

Volatility Regime (Parkinson / ATR): Filter signals during high volatility spikes or compressions.
Experimentation: Try weighting these supporting signals to dynamically confirm or filter skew/kurt crossovers and see if false signals decrease on 2–5 minute charts.
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Dynamic Thresholds & Scaling
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Right now, the extreme skew signal is triggered at a fixed level (skew ≤ -1.7). Future improvements could include:

Adaptive thresholds: Scale extreme skew levels based on recent standard deviation or intraday volatility.

Kurtosis thresholds: Introduce a cutoff for kurtosis to identify “fat-tail” events.
Experimentation: Backtest different adaptive thresholds for both skew and kurt, and see how it affects the precision vs. frequency of signals.
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Multi-Timeframe or Combined Oscillator
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Skew/kurt signals could be combined across multiple intraday timeframes (e.g., 1-min, 3-min, 5-min) to improve confirmation.

Create a composite oscillator that blends short-term and slightly longer-term skew/kurt values to reduce noise.
Experimentation: Compare a single timeframe approach vs multi-timeframe composite, and measure signal reliability and lag.

I'm leaving this open so anyone can experiment with it as this project may be on the backburner, but these are my thoughts so far